1. Technical Field
The present invention relates to a method and system for determining the speed of induction motors in general, and in particular to a neural network-based method and system for determining the dynamic speed of induction motors.
2. Description of the Related Art
Induction motors are commonly found in power plants and various types of manufacturing facilities throughout the world. An induction motor typically includes a stationary stator and a rotatable rotor. The ability to accurately determine the speed of a rotating rotor with respect to a stationary stator within an induction motor is vitally important to the every day operations of induction motors. However, it is generally undesirable to introduce transducers or other physical sensors for measuring the speed or the position of a rotating rotor because of the additional cabling and increased costs. For example, for a small motor, such as 5 horsepower or less, the cost of speed sensor installation could be comparable to the cost of the motor itself. Also, the probability of failure of the speed sensor is higher than the probability of failure of the small motor.
Hence, sensorless rotor speed estimation methods (or non-intrusive rotor speed estimation methods) have emerged as a cost-effective alternative over the sensor-based rotor speed estimation methods. Sensorless rotor speed estimation methods generally fall under one of the following two categories, this is, either by using an induction motor model, or by analyzing the rotor slot harmonic (RSH) content of the stator current waveform.
The model based speed estimation methods are inconvenient because they have to rely on detailed motor parameters, i.e., a priori knowledge of the motor""s electrical (and in some cases mechanical) characteristics, in order to operate properly. However, those parameters are typically available only to the designer of the motor. Further, many motor based speed estimation methods assume linear motor models and time invariant parameters, which leads to poor speed estimation. The RSH based speed estimation methods are not completely acceptable either because they sometimes perform poorly at low motor speeds due to difficulty in tracking low frequency harmonics. Additionally, RSH based speed estimation methods require the calculation of the Fast Fourier Transform (FFT) of the stator current, and as a result, RSH based speed estimation methods inherit all the typical limitations of an FFT based scheme. For example, for an accurate calculation of the RSH, a relatively higher frequency resolution is required (typically in the range of 1-2 Hz), which implies longer data windows. Also, for certain rotor-stator slot combinations, primarily those at lighter loads, the RSH may not be readily detectable. While the transient speed can be calculated by using a moving window, it leads to poor time localization of the estimated speed response. Moreover, FFT based schemes are computationally burdensome for real-time implementation, and more expensive data processing equipment is needed, which would offset any cost advantages the RSH based speed estimation methods might deliver.
Consequently, it would be desirable to provide an improved sensorless method and system for determining the dynamic speed of a rotating rotor.
In accordance with a preferred embodiment of the present invention, a non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed xcfx89RSH calculator derived from the actual current and voltage measurements. The training of the neural network-based adaptive filter takes place off-line; thus, time lags resulting from the FFT-based estimation can be compensated. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations and for adaptation to new motors, though the on-line adaptation scheme needs only to be used infrequently.
All objects, features, and advantages of the present invention will become apparent in the following detailed written description.